Embeddings tables aren't hard on the GPU (being only a lookup table), and the output softmax still requires you do the full matrix-multiply. The label may be sparse, but the computation is far from sparse.
Check figure 6. of : https://arxiv.org/pdf/1906.00091.pdf
Embeddings are used to lookup sparse features, so you have those pesky data-dependent lookups.
They may be a bottleneck, but the alternative is worse -- you can't fit complex models with large vocabularies into GPU memory using sparse one-hot encodings.
[1] https://morfessor.readthedocs.io/en/latest/
You also will get much more meaningful embeddings from summing embeddings of part of the word.
Technically, the sparse one-hot encoding is the most efficient in terms of memory footprint. You simply store the non-zero coordinates.
The problem in practice for GPUs is that sparse vector/matrix operations are too inefficient.
The whole point of something like this paper is to skip the entire 'densification' step and to directly deal with the sparse matrix input as a sparse matrix. The LSH is used in this paper improves on directly using SpMSpV, as that is also inefficient on CPUs, although to a lesser extent than GPUs.
See libraries like magnitude for proper embedding lookup implementations